#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Tests for algorithms related to association rules. """ import pytest from efficient_apriori.apriori import apriori from efficient_apriori.rules import Rule from efficient_apriori.itemsets import ItemsetCount def test_api(): transactions = [ ("a", "c", "e"), ("a", "c", "e"), ("a", "d", "e"), ("b", "d", "e"), ("b", "d", "f"), ("b", "c", "f"), ("b", "c", "f"), ] itemsets, rules = apriori(transactions, 0.2, 0.2) assert itemsets[1] == {("a",): 3, ("c",): 4, ("e",): 4, ("d",): 3, ("b",): 4, ("f",): 3} assert all(isinstance(rule, Rule) for rule in rules) for count, itemsets_dict in itemsets.items(): assert isinstance(itemsets_dict, dict) for itemset, count in itemsets_dict.items(): actual_count = sum(1 if set(itemset).issubset(set(trans)) else 0 for trans in transactions) assert count == actual_count itemsets, rules = apriori(transactions, 0.2, 0.2, output_transaction_ids=True) for count, itemsets_dict in itemsets.items(): assert isinstance(itemsets_dict, dict) for itemset, counter in itemsets_dict.items(): assert isinstance(counter, ItemsetCount) actual_count = sum(1 if set(itemset).issubset(set(trans)) else 0 for trans in transactions) assert counter.itemset_count == actual_count def test_against_R_implementation_1(): """ The following R-code was used: > install.packages("arules") > col1 = c("a", "a", "a", "b", "b", "b", "b") > col2 = c("c", "c", "d", "d", "d", "c", "c") > col3 = c("e", "e", "e", "e", "f", "f", "f") > df = data.frame(col1, col2, col3) > df <- data.frame(sapply(df, as.factor)) > rules <- apriori(df, parameter = list(supp = 0.2, conf = 0.2)) > inspect(head(rules, by = "confidence")) """ transactions = [ ("a", "c", "e"), ("a", "c", "e"), ("a", "d", "e"), ("b", "d", "e"), ("b", "d", "f"), ("b", "c", "f"), ("b", "c", "f"), ] itemsets, rules = apriori(transactions, 0.2, 0.2) assert Rule(("a",), ("e",)) in rules for rule in rules: if rule == Rule(("a",), ("e",)): assert abs(rule.support - 0.4285714) < 10e-7 assert rule.confidence == 1 if rule == Rule(("c", "e"), ("a",)): assert abs(rule.support - 0.2857143) < 10e-7 assert rule.confidence == 1 if rule == Rule(("e",), ("a",)): assert abs(rule.support - 0.4285714) < 10e-7 assert rule.confidence == 3 / 4 def test_against_R_implementation_2(): """ The following R-code was used: > install.packages("arules") > col1 = c("b", "b", "c", "b", "a", "a", "b", "c", "b", "b", "a", "b", "a", "a", "a", "c", "b", "a", "b", "b", "b", "c", "a", "c", "a", "a", "c", "a", "b", "b", "a", "c") > col2 = c("e", "f", "e", "e", "f", "e", "d", "f", "e", "e", "e", "d", "e", "e", "f", "d", "d", "d", "e", "f", "f", "d", "d", "f", "e", "e", "f", "f", "f", "d", "e", "e") > col3 = c("g", "i", "j", "i", "i", "j", "i", "h", "g", "j", "g", "h", "i", "h", "g", "h", "g", "j", "h", "i", "g", "g", "i", "h", "h", "h", "h", "g", "j", "i", "g", "g") > df = data.frame(col1, col2, col3) > df <- data.frame(sapply(df, as.factor)) > rules <- apriori(df, parameter = list(supp = 0.2, conf = 0.2)) > inspect(head(rules, by = "confidence")) """ transactions = [ ("b", "e", "g"), ("b", "f", "i"), ("c", "e", "j"), ("b", "e", "i"), ("a", "f", "i"), ("a", "e", "j"), ("b", "d", "i"), ("c", "f", "h"), ("b", "e", "g"), ("b", "e", "j"), ("a", "e", "g"), ("b", "d", "h"), ("a", "e", "i"), ("a", "e", "h"), ("a", "f", "g"), ("c", "d", "h"), ("b", "d", "g"), ("a", "d", "j"), ("b", "e", "h"), ("b", "f", "i"), ("b", "f", "g"), ("c", "d", "g"), ("a", "d", "i"), ("c", "f", "h"), ("a", "e", "h"), ("a", "e", "h"), ("c", "f", "h"), ("a", "f", "g"), ("b", "f", "j"), ("b", "d", "i"), ("a", "e", "g"), ("c", "e", "g"), ] itemsets, rules = apriori(transactions, 0.2, 0.2) for rule in rules: if rule == Rule(("a",), ("e",)): assert abs(rule.support - 0.21875) < 10e-7 assert abs(rule.confidence - 0.5833333) < 10e-7 if rule == Rule(("e",), ("a",)): assert abs(rule.support - 0.21875) < 10e-7 assert abs(rule.confidence - 0.5000000) < 10e-7 def test_against_R_implementation_3(): """ The following R-code was used: > install.packages("arules") > col1 = c("b", "b", "c", "a", "b", "b", "a", "a", "b", "b", "a", "a", "c", "b", "a", "c") > col2 = c("e", "d", "e", "e", "e", "e", "d", "e", "e", "e", "d", "e", "e", "e", "d", "e") > col3 = c("i", "g", "h", "j", "i", "g", "h", "j", "i", "g", "j", "i", "j", "j", "i", "i") > df = data.frame(col1, col2, col3) > df <- data.frame(sapply(df, as.factor)) > rules <- apriori(df, parameter = list(supp = 0.2, conf = 0.2)) > inspect(head(rules, by = "confidence")) """ transactions = [ ("b", "e", "i"), ("b", "d", "g"), ("c", "e", "h"), ("a", "e", "j"), ("b", "e", "i"), ("b", "e", "g"), ("a", "d", "h"), ("a", "e", "j"), ("b", "e", "i"), ("b", "e", "g"), ("a", "d", "j"), ("a", "e", "i"), ("c", "e", "j"), ("b", "e", "j"), ("a", "d", "i"), ("c", "e", "i"), ] itemsets, rules = apriori(transactions, 0.2, 0.2) for rule in rules: if rule == Rule(("b",), ("e",)): assert abs(rule.support - 0.3750) < 10e-7 assert abs(rule.confidence - 0.8571429) < 10e-7 if rule == Rule(("i",), ("e",)): assert abs(rule.support - 0.3125) < 10e-7 assert abs(rule.confidence - 0.8333333) < 10e-7 if rule == Rule(("j",), ("e",)): assert abs(rule.support - 0.2500) < 10e-7 assert abs(rule.confidence - 0.8000000) < 10e-7 if rule == Rule(("e",), ("b",)): assert abs(rule.support - 0.3750) < 10e-7 assert abs(rule.confidence - 0.5000000) < 10e-7 def test_minimal_input(): """ The with some minimal inputs, and make sure the correct errors are raised. """ transactions = [] itemsets, rules = apriori(transactions, 0.2, 0.2) assert itemsets == {} and rules == [] with pytest.raises(ValueError): itemsets, rules = apriori(transactions, -0.2, 0.2) with pytest.raises(ValueError): itemsets, rules = apriori(transactions, 0.2, -0.2) with pytest.raises(ValueError): itemsets, rules = apriori(transactions, "asdf", 1) itemsets, rules = apriori([(1, 2), (1, 2), (1, 3)], 1, 1) itemsets, rules = apriori([(1, 2), (1, 2), (1, 3)], 1.0, 1.0) def test_iterator_input(): """ Minimal test using transactions from iterators. """ empty_iterator = iter(()) transactions = empty_iterator itemsets, rules = apriori(transactions, 0.2, 0.2) assert itemsets == {} and rules == [] transactions = [(1, 2), (1, 2), (1, 3), (1, 4), (1, 3)] transactions_iter = iter(transactions) itemsets1, rules1 = apriori(transactions_iter, 0.2, 1) itemsets2, rules2 = apriori(transactions, 0.2, 1) assert len(rules1) == len(rules2) for i in range(len(rules1)): assert rules1[i] == rules2[i] def test_empty_H_1(): """ An example of the case where there are itemsets without any Rule with single item in right hand side that satifies the required minimum confidence. The issue is raised in #57. """ # The results are received from commit 01d174379c51758aa2f6d2926b473124928dc631 true_itemsets_raw = {1: {(1,): 4, (2,): 5, (3,): 4}, 2: {(1, 2): 4, (1, 3): 3, (2, 3): 4}, 3: {(1, 2, 3): 3}} true_rules = [ Rule((2,), (1,), 4, 5, 4, 5), Rule((1,), (2,), 4, 4, 5, 5), Rule((3,), (2,), 4, 4, 5, 5), Rule((2,), (3,), 4, 5, 4, 5), Rule((1, 3), (2,), 3, 3, 5, 5), ] transactions = [(1, 2, 3), (1, 2, 3), (1, 2, 3), (1, 2), (2, 3)] itemsets_raw, rules = apriori(transactions, 0.4, 0.8) assert itemsets_raw == true_itemsets_raw assert all(rule == true_rule for rule, true_rule in zip(rules, true_rules)) if __name__ == "__main__": pytest.main(args=[".", "--doctest-modules", "-v"])